1 code implementation • 17 Jun 2022 • Yuanpei Chen, Yaodong Yang, Tianhao Wu, Shengjie Wang, Xidong Feng, Jiechuang Jiang, Stephen Marcus McAleer, Hao Dong, Zongqing Lu, Song-Chun Zhu
In this study, we propose the Bimanual Dexterous Hands Benchmark (Bi-DexHands), a simulator that involves two dexterous hands with tens of bimanual manipulation tasks and thousands of target objects.
no code implementations • 4 Mar 2022 • Tianhao Wu, Fangwei Zhong, Yiran Geng, Hongchen Wang, Yongjian Zhu, Yizhou Wang, Hao Dong
we formulate the dynamic grasping problem as a 'move-and-grasp' game, where the robot is to pick up the object on the mover and the adversarial mover is to find a path to escape it.
no code implementations • 19 Dec 2021 • Mingxin Yu, Lin Shao, Zhehuan Chen, Tianhao Wu, Qingnan Fan, Kaichun Mo, Hao Dong
Part assembly is a typical but challenging task in robotics, where robots assemble a set of individual parts into a complete shape.
no code implementations • 10 Dec 2021 • Jiahao Huang, Weiping Ding, Jun Lv, Jingwen Yang, Hao Dong, Javier Del Ser, Jun Xia, Tiaojuan Ren, Stephen Wong, Guang Yang
The dual discriminator design aims to improve the edge information in MRI reconstruction.
no code implementations • 1 Dec 2021 • Yian Wang, Ruihai Wu, Kaichun Mo, Jiaqi Ke, Qingnan Fan, Leonidas Guibas, Hao Dong
Perceiving and interacting with 3D articulated objects, such as cabinets, doors, and faucets, pose particular challenges for future home-assistant robots performing daily tasks in human environments.
no code implementations • 29 Sep 2021 • Yuchen Liu, Yali Du, Runji Lin, Hangrui Bi, Mingdong Wu, Jun Wang, Hao Dong
Model-based RL is an effective approach for reducing sample complexity.
1 code implementation • 26 Aug 2021 • Yixiao Guo, Jiawei Liu, Guo Li, Luo Mai, Hao Dong
When it comes to customising these algorithms for real-world applications, none of the existing libraries can offer both the flexibility of developing custom pose estimation algorithms and the high-performance of executing these algorithms on commodity devices.
no code implementations • ICCV 2021 • Yunze Liu, Qingnan Fan, Shanghang Zhang, Hao Dong, Thomas Funkhouser, Li Yi
Another approach is to concatenate all the modalities into a tuple and then contrast positive and negative tuple correspondences.
Ranked #33 on
Semantic Segmentation
on NYU Depth v2
no code implementations • ICLR 2022 • Ruihai Wu, Yan Zhao, Kaichun Mo, Zizheng Guo, Yian Wang, Tianhao Wu, Qingnan Fan, Xuelin Chen, Leonidas Guibas, Hao Dong
In this paper, we propose object-centric actionable visual priors as a novel perception-interaction handshaking point that the perception system outputs more actionable guidance than kinematic structure estimation, by predicting dense geometry-aware, interaction-aware, and task-aware visual action affordance and trajectory proposals.
1 code implementation • 19 Apr 2021 • Jie Ren, Yewen Li, Zihan Ding, Wei Pan, Hao Dong
However, grasping distinguishable skills for some tasks with non-unique optima can be essential for further improving its learning efficiency and performance, which may lead to a multimodal policy represented as a mixture-of-experts (MOE).
no code implementations • 29 Mar 2021 • Pan Wang, Zhifeng Gong, Shuo Wang, Hao Dong, Jialu Fan, Ling Li, Peter Childs, Yike Guo
To modify a design semantic of a given product from personalised brain activity via adversarial learning, in this work, we propose a deep generative transformation model to modify product semantics from the brain signal.
no code implementations • 22 Feb 2021 • Zhiyuan Ning, Ziyue Qiao, Hao Dong, Yi Du, Yuanchun Zhou
Knowledge graph embedding (KGE) models learn to project symbolic entities and relations into a continuous vector space based on the observed triplets.
no code implementations • 24 Dec 2020 • Yunze Liu, Li Yi, Shanghang Zhang, Qingnan Fan, Thomas Funkhouser, Hao Dong
Self-supervised representation learning is a critical problem in computer vision, as it provides a way to pretrain feature extractors on large unlabeled datasets that can be used as an initialization for more efficient and effective training on downstream tasks.
1 code implementation • 18 Nov 2020 • Minghang Zheng, Peng Gao, Renrui Zhang, Kunchang Li, Xiaogang Wang, Hongsheng Li, Hao Dong
In this paper, a novel variant of transformer named Adaptive Clustering Transformer(ACT) has been proposed to reduce the computation cost for high-resolution input.
no code implementations • 30 Sep 2020 • Chu-ran Wang, Jing Li, Fandong Zhang, Xinwei Sun, Hao Dong, Yizhou Yu, Yizhou Wang
Mammogram benign or malignant classification with only image-level labels is challenging due to the absence of lesion annotations.
1 code implementation • 21 Sep 2020 • Jian Mei, Hao Dong
DongNiao International Birds 10000 (DIB-10K) is a challenging image dataset which has more than 10 thousand different types of birds.
no code implementations • 20 Sep 2020 • Qingrui Zhang, Hao Dong, Wei Pan
More importantly, the existing multi-agent reinforcement learning (MARL) algorithms cannot ensure the closed-loop stability of a multi-agent system from a control-theoretic perspective, so the learned control polices are highly possible to generate abnormal or dangerous behaviors in real applications.
1 code implementation • 18 Sep 2020 • Zihan Ding, Tianyang Yu, Yanhua Huang, Hongming Zhang, Guo Li, Quancheng Guo, Luo Mai, Hao Dong
RLzoo provides developers with (i) high-level yet flexible APIs for prototyping DRL agents, and further customising the agents for best performance, (ii) a model zoo where users can import a wide range of DRL agents and easily compare their performance, and (iii) an algorithm that can automatically construct DRL agents with custom components (which are critical to improve agent's performance in custom applications).
2 code implementations • NeurIPS 2020 • Jialei Huang, Guanqi Zhan, Qingnan Fan, Kaichun Mo, Lin Shao, Baoquan Chen, Leonidas Guibas, Hao Dong
Analogous to buying an IKEA furniture, given a set of 3D parts that can assemble a single shape, an intelligent agent needs to perceive the 3D part geometry, reason to propose pose estimations for the input parts, and finally call robotic planning and control routines for actuation.
1 code implementation • ICLR 2020 • Jie Fu, Xue Geng, Zhijian Duan, Bohan Zhuang, Xingdi Yuan, Adam Trischler, Jie Lin, Chris Pal, Hao Dong
To our knowledge, existing methods overlook the fact that although the student absorbs extra knowledge from the teacher, both models share the same input data -- and this data is the only medium by which the teacher's knowledge can be demonstrated.
2 code implementations • ECCV 2020 • Yihao Zhao, Ruihai Wu, Hao Dong
Cycle-consistency loss is a widely used constraint for such problems.
no code implementations • 22 Nov 2019 • Guanqi Zhan, Yihao Zhao, Bingchan Zhao, Haoqi Yuan, Baoquan Chen, Hao Dong
By mapping the discrete label-specific attribute features into a continuous prior distribution, we leverage the advantages of both discrete labels and reference images to achieve image manipulation in a hybrid fashion.
no code implementations • 11 Jun 2018 • Hao Dong, Shuai Li, Dongchang Xu, Yi Ren, Di Zhang
The training of Deep Neural Networks usually needs tremendous computing resources.
no code implementations • 19 May 2018 • Simiao Yu, Hao Dong, Pan Wang, Chao Wu, Yike Guo
Bionic design refers to an approach of generative creativity in which a target object (e. g. a floor lamp) is designed to contain features of biological source objects (e. g. flowers), resulting in creative biologically-inspired design.
no code implementations • 20 Nov 2017 • Hao Dong, Chao Wu, Zhen Wei, Yike Guo
However, current architecture of deep networks suffers the privacy issue that users need to give out their data to the model (typically hosted in a server or a cluster on Cloud) for training or prediction.
2 code implementations • 26 Jul 2017 • Hao Dong, Akara Supratak, Luo Mai, Fangde Liu, Axel Oehmichen, Simiao Yu, Yike Guo
Deep learning has enabled major advances in the fields of computer vision, natural language processing, and multimedia among many others.
2 code implementations • ICCV 2017 • Hao Dong, Simiao Yu, Chao Wu, Yike Guo
In this paper, we propose a way of synthesizing realistic images directly with natural language description, which has many useful applications, e. g. intelligent image manipulation.
no code implementations • 19 May 2017 • Simiao Yu, Hao Dong, Guang Yang, Greg Slabaugh, Pier Luigi Dragotti, Xujiong Ye, Fangde Liu, Simon Arridge, Jennifer Keegan, David Firmin, Yike Guo
Fast Magnetic Resonance Imaging (MRI) is highly in demand for many clinical applications in order to reduce the scanning cost and improve the patient experience.
no code implementations • 10 May 2017 • Hao Dong, Guang Yang, Fangde Liu, Yuanhan Mo, Yike Guo
In this context, a reliable fully automatic segmentation method for the brain tumor segmentation is necessary for an efficient measurement of the tumor extent.
no code implementations • 20 Mar 2017 • Hao Dong, Jingqing Zhang, Douglas McIlwraith, Yike Guo
We demonstrate that %the capability of our method to understand the sentence descriptions, so as to I2T2I can generate better multi-categories images using MSCOCO than the state-of-the-art.
7 code implementations • 12 Mar 2017 • Akara Supratak, Hao Dong, Chao Wu, Yike Guo
This demonstrated that, without changing the model architecture and the training algorithm, our model could automatically learn features for sleep stage scoring from different raw single-channel EEGs from different datasets without utilizing any hand-engineered features.
Ranked #2 on
Sleep Stage Detection
on Sleep-EDF
(using extra training data)
no code implementations • 10 Jan 2017 • Hao Dong, Paarth Neekhara, Chao Wu, Yike Guo
It's useful to automatically transform an image from its original form to some synthetic form (style, partial contents, etc.
no code implementations • 15 Oct 2016 • Hao Dong, Akara Supratak, Wei Pan, Chao Wu, Paul M. Matthews, Yike Guo
Use of this recording configuration with neural network deconvolution promises to make clinically indicated home sleep studies practical.
1 code implementation • 23 Jun 2016 • Wei Pan, Hao Dong, Yike Guo
We proposed regularisers which support a simple mechanism of dropping neurons during a network training process.